The goal of the Bioinformatics core is to provide expert computational analysis of molecular profiling (expression and NMR) data in order to determine the molecular signatures predictive of diagnosis and outcome in Soft Tissue Sarcoma (STS). The core will not only provide computational/statistical analysis but will build and maintain the data infrastructure needed by the various projects, whose work will lead to the definition of new marker sets, mechanistic hypotheses and possible identification of new drug targets. The core will also facilitate integration of research in the projects by enabling the sharing of the various datasets collected. Specifically, it will perform the following tasks. 1) Statistical analysis of microarray expression data including: error analysis, normalization, unsupervised clustering analysis, differential gene analysis and multivariate class prediction. These methods will be applied in the following cases: a. Cluster and differential gene expression analysis of sarcoma subtypes to classify sarcoma tissue samples based on their similarity in gene expression, to identify potential diagnostic/prognostic markers and to determine the relevant genes for subsequent pathway analysis;b. Expression analysis of SYT-SSX regulated genes along with the analysis of the respective promoters and expression based survival prediction of Synovial Sarcomas;c. Supervised learning analysis of clinical variables such as distant recurrence and survival, the object being to generate expression based predictors. 2) Statistical analysis of NMR data obtained from Liposarcoma samples, including prediction of Liposarcoma subtypes and sample clinical variables (outcome/survival) using supervised machine learning techniques. Development of integrated (microarray/NMR) molecular profiling analysis to develop prognostic marker sets. 3) Pathway analysis of molecular profiling data. Integrating data from (1) and (2) with pathway data to: a. Elucidate the biological basis of tumor subtypes;b. Find new potential drug targets. 4) To develop an online repository of microarray expression data along with a database of annotation information and clinical data. Integrate and make available the large collection of datasets to be collected. 5) To develop a patient data tracking system for multi-institutional clinical trials.